Background and Objectives: Aydughmush dam was built on Aydughmush River at 19 km in southwest of Mianeh City. The dam is multipurpose and its main aims are the spring floods control, supplying potable water for villages and providing irrigation water. Different pollutants which probably discharge to the river finally enter to the dam reservoir so; this study focuses on evaluating the quality of the dam reservoir.Materials and Methods: In this cross sectional study standard field parameters including dissolved oxygen, temperature, Biochemical and chemical oxygen Demand, Most Probable Number of Coliforms, Fecal Coliform, Turbidity, Total Dissolved Solids, Total Solids, pH, conductivity and others were measured at eight different stations during the spring and summer in 2010. Sampling points were selected on the basis of their importance. Water quality index was calculated using water quality index calculator given by National Sanitation Foundation (NSF) information system.Results: The highest value of WQI of the samples was 84.89 in A3 station in July while the lowest value was 67.96 in A2 station in May. The lower value of WQI has been found mainly due to the slightly lower value of DO in the dam reservoir water. Most of the water samples were found within Good category of National Sanitation Foundation Water Quality Index (NSF-WQI). Comparison of the measured parameters based on the sampling stations and various months by variance and t-student analysis showed a significant relationship for some parameters (P<0.05). Nutrient budget determination indicates that the concentration of phosphate, nitrite, nitrate and ammonia at inlet are higher than outlet of the dam reservoir.Conclusion: The calculated (WQI) showed good water quality. Based on the results of NSFQWI calculations, the dam reservoir water quality is suitable for various purposes.

Wetlands are one of the most prominent ecosystems in the world. These vital and diverse habitats are among life-giving systems that have no alternative. However, none of the world's ecosystems have experienced human-centered injuries and damages as much as wetlands. One of the main threats to Gilan wetlands are human factors such as urban, domestic and industrial wastewater, overfishing and converting wetland marginal lands into agricultural lands. In this study, RGB images were used to assess the water quality parameters of Anzali wetland (Beheshti Island Station) and the related data were compared to the values obtained from the TSS measurement. Based on the obtained data, the intensity of red color (R) in the macroscopic images (with the naked eye) from the wetland can be an environmental indicator to measure TSS concentration. The results of RGB analysis for red color with a correlation coefficient of 0. 8513, for green color (G) with a correlation coefficient of 0. 832 and for blue color (B) with a correlation coefficient of 0. 663 were obtained. Finally, a correlation coefficient (R2=0/8035) between the decrease of RGB values and the increase of TSS concentration was obtained. Other parameters such as pH and Secchi Depth test were also measured in this study.

Rivers are considered one of the most important resources of providing fresh water. Restrictions faced by such resources underline the necessity of preserving their quality. Water quality indices are usually resorted to qualitatively monitor water resources. Any of such indices is calculated with regard to a series of specific qualitative parameters. The process of sampling and quantification of the aforementioned parameters are, however, time-consuming and costly. Finding a reliable method comprised of minimum qualitative parameters could be, therefore, of great help in classifying water quality. As an alternative to the common NSFQWI, the advantages of the Probabilistic Neural Network (PNN) as a classifier are used in the present study to classify water quality of Karun River. In order to fulfill this objective, the qualitative statistics of 172 samples were used in a way that qualitative parameters and water quality classes derived from NSFWQI are used respectively as the input and output of the model. The assessment criteria of error rate, error value, accuracy and Spearman’ s correlation coefficient were used to evaluate the performance of PNN model. The results showed that through making use of merely three parameters of turbidity, fecal coliform and total dissolved solids, PNN model is capable of classifying water quality with the accuracy of 94. 365% and 90. 7769% at two stages of training and test respectively which in turn indicates considerable accuracy of PNN in determining water quality classification.

Background and objectives: Precise forecasting of water quality (WQ) parameters, specifically PS (potential salinity), is critical for sustainable water utilization. In water-stressed regions like the Karun River in Iran, effective monitoring and prediction of the PS is not only important but also critical because of anthropogenic activities, climate change, and reduced inflows of freshwater. Therefore, effective machine learning (ML) models and appropriate input data is very important for monitoring and predicting WQ parameters. However, the influencing factors exhibit complex and non-linear relationships, and multicollinearity in the datasets makes it challenging for traditional ML models to address the problem. Limitations, thus, can result in inaccurate predictions, which obstruct the establishment of sustainable water management strategies. As mentioned above, accurate forecasting of PS is essential for water and soil conservation, because PS helps mitigate salinity-related degradation of agricultural lands and ensure the sustainability of vital ecosystems. This study supports the development of effective conservation strategies to maintain soil productivity and WQ in vulnerable regions by providing reliable predictions. To address these issues, the present study introduces a new hybrid model, IKRidge-GRM, which inherits the advantages of improved kernel ridge regression (IKRidge) and generalized ridge regression (GRM). The hybrid model integrates IKRidge's improved capacity to identify non-linearity with GRM's resilience against multicollinearity problems to improve the predictive performance of the PS prediction. This unique framework offers improved stability and interpretability of results, as well as increases forecast accuracy, making it a helpful tool for environmental monitoring and decision-making. The proposed strategy could aid policymakers and water resource managers in designing reasonable strategies to alleviate salinity issues, protect aquatic ecosystems, and ensure the long-term survival of vital water sources like the Karun River.Materials and methods: This study introduces a novel hybrid ML model based on two regression techniques, namely: generalized ridge regression (GRM) and improved kernel ridge regression (IKRidge), called IKRidge-GRM. The GRM effectively addresses multicollinearity and overfitting issues using the iteratively reweighted least squares (IRLS) process. On the other hand, IKRidge incorporates a wavelet kernel function, optimized through the INFO algorithm, and the regularized locally weighted (RLW) approach, enabling it to capture complex, non-linear patterns in the data with high precision. This combination of techniques allows the hybrid model to overcome the limitations of traditional ML methods, making it particularly suitable for handling the intricate relationships inherent in WQ datasets. To further enhance the model's predictive accuracy, the IKRidge-GRM framework integrates a light gradient boosting machine (LGBM) for feature selection. It reduces dimensionality by identifying the most relevant input variables while eliminating redundant or irrelevant features.Additionally, the model employs multivariate variational mode decomposition (MVMD) to decompose the input data into high- and low-frequency components, allowing it to capture both short-term fluctuations and long-term trends in WQ parameters. The study utilized an extensive dataset comprising 48 years of monthly WQ data collected from the Farisat station on the Karun River. Nine keys WQ parameters, including magnesium (Mg), sulfate (SO42−), calcium (Ca), discharge (Q), sodium (Na), bicarbonate (HCO3), chloride (Cl), electrical conductivity (EC), total dissolved solids (TDS) and pH, were used as inputs to forecast the PS three months ahead. Results: The proposed IKRidge-GRM model accurately predicted PS values at the Farisat station, significantly outperforming baseline models (Ridge, DELM, and LSSVM) and their MVMD-enhanced versions. By leveraging its hybrid architecture and advanced feature extraction techniques, the MVMD-IKRidge-GRM model achieved remarkable results during the testing phase, with the highest correlation coefficient (R = 0.977), the lowest RMSE (0.956), and the lowest MAPE (4.521). These metrics indicate the model's superior predictive accuracy and reliability in handling complex, non-linear relationships. The model also achieved high IA (0.988) and KGE (0.948) scores, underscoring its robustness and effectiveness in capturing the intricate dynamics of the PS variations. These results highlight the model's ability to uncover hidden patterns in the data and provide highly accurate predictions, even in challenging scenarios involving multicollinearity and non-linear dependencies. The model's exceptional performance was further confirmed by visual evaluations such as scatter plots, relative error plots, and Taylor diagrams. Scatter plots demonstrated that the MVMD-IKRidge-GRM model's predictions closely aligned with measured values, with minimal prediction intervals and narrow error distributions, reflecting its precision and consistency. Relative error plots revealed that the model exhibited the most compact and symmetric error distribution, with minimal bias and variability. Relative error plots also indicated the models’ ability to generalize well across different data points. Taylor diagrams provided evidence of the model's strong agreement with reference data, showcasing its ability to balance accuracy, variability representation, and error minimization effectively. Residual analysis further confirmed the model's precision and reliability. Among all the models tested, the MVMD-IKRidge-GRM model achieved the smallest mean residual (-0.0073) and the lowest standard deviation (0.0613), demonstrating its ability to minimize prediction errors consistently. This level of precision is critical for practical applications, as it ensures that the model can provide reliable forecasts for decision-making in water resource management. The model's ability to integrate advanced regression techniques, feature selection, and frequency decomposition enhances its predictive capabilities. The ability also establishes the proposed model as a robust framework for addressing complex environmental challenges. These findings emphasized the potential of the MVMD-IKRidge-GRM model as a powerful tool for sustainable water resource management, particularly in regions like the Karun River basin, where accurate and reliable predictions are essential for mitigating environmental degradation and ensuring long-term ecological balance.Conclusion: The IKRidge-GRM model predicted PS values at the Farisat station on the Karun River. The findings demonstrated high accuracy and reliability across all evaluation metrics. The IKRidge-GRM model has the ability to uncover hidden patterns in complex, non-linear datasets. Its capacity to deliver precise predictions also highlights its potential as a valuable tool for environmental monitoring and management. By integrating advanced regression techniques, such as improved kernel ridge regression (IKRidge) and generalized ridge regression (GRM), with innovative feature selection and decomposition methods like light gradient boosting machine (LGBM) and multivariate variational mode decomposition (MVMD), the model effectively addresses challenges such as multicollinearity, overfitting, and non-linear relationships. This comprehensive framework ensures that the IKRidge-GRM model achieves superior predictive performance and maintains robustness and adaptability across diverse environmental conditions. This study emphasizes the importance of combining advanced ML techniques with effective preprocessing methods to develop reliable models for analyzing and forecasting complex environmental data. Integrating feature selection and frequency decomposition enhances the model's ability to extract meaningful information from high-dimensional datasets. This integration also enable the models to capture both short-term fluctuations and long-term trends in WQ parameters better. Such capabilities are essential for addressing the multifaceted challenges posed by environmental degradation, particularly in regions like the Karun River basin, where water resources are under significant stress due to anthropogenic activities and climate change.

This paper presents the application of recently introduced water cycle algorithm (WCA) to optimize the parameters of exact and approximate induction motor from the nameplate data. Considering that induction motors are widely used in industrial applications, these parameters have a significant effect on the accuracy and efficiency of the motors and, ultimately, the overall system performance. Therefore, it is essential to develop algorithms for the parameter estimation of the induction motor. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. The objective function is defined as the minimization of the real values of the relative error between the measured and estimated torques of the machine in different slip points. The proposed WCA approach has been applied on two different sample motors. Results of the proposed method have been compared with other previously applied Meta heuristic methods on the problem, which show the feasibility and the fast convergence of the proposed approach.

This study attempts to fill the critical need for comprehensive data on pond water quality in the central region of rural India, with an emphasis on the industrial and agricultural zone of Chhattisgarh’s Raipur district. Geospatial tools were used to map pollution distribution and identify contamination hotspots across the district. The study employs geospatial analysis and mapping methods to assess physicochemical factors, such as pH, EC, TDS, DO, and concentrations of metals, alongside the Water Quality Index (WQI) and other water quality indicators to evaluate the overall impact of industrial and agricultural pollution on pond water quality. Ponds are increasingly in danger due to pollution from domestic waste, industrial discharge, and agricultural runoff, even though they are essential for everyday tasks like irrigation and drinking. Twenty ponds were selected for analysis based on their proximity to industrial zones and agricultural activities. Water samples were collected and analysed for key physicochemical parameters and specific metal contaminants for WQI. The total WQI score was 115.74, indicating severe contamination across the sampled ponds. This high WQI score underscores the urgent need for remediation efforts to address pollution and protect public health. The results offer insightful information about the declining condition of pond water quality, emphasizing the need for prompt action and long-term sustainable management strategies. Bioremediation methods such as phytoremediation, microbial treatment, and adsorption techniques (e.g., activated carbon) can effectively remediate water contaminated by industrial and agricultural pollutants, improving water quality sustainably and cost-effectively.

In order to study the effects of water stress on growth parameter of wheat at different phonological stages, this experiment was conducted as split plot design with four replication. Main plot were include: jointing stage, anthesis and seed filling period. Sub plot were: Irrigation at field capacity, 75% field capacity,50% field capacity,25% field capacity. The results showed the highest and lowest dry matter accumulation obtained with applying water stress treatment at jointing stage and seed filling stage, respectively. With increasing water stress, the amount of dry matter decreased significantly. Also in greater water stress the value of LAI and NAR decreased.

In areas such as the Strait of Hormuz analyzing the water flux is of great importance since in these areas subsurface currents can cause flux movement which in turn can cause lots of problems in marine transportation especially in submarine navy. In this study water current characteristic and flux parameters ((are analyzed in the area of the Strait of Hormuz using HD module. For this purpose, current characteristic within the area was simulated using Mike21 numerical model. The results show that water flux component along the Strait of Hormuz (P component) changes within the range of -1.4 to 0.6 m3/(s⁄m) and the water flux component along the width of the strait of Hormuz (Q component) changes within the range of -0.6 to 0.5 m3/(s⁄m). In general, the results of this study show that since the P component is related to surface velocity, this component will decrease and the subsurface flux will increase if the surface velocity decreases. Designing such patterns (pattern of water flux) is crucial in navy, drawing naval maps, and drawing submarine subsurface maps.

Introduction There are two approaches for water quality standardization and monitoring the pollution loads discharged into the water bodies, like rivers and estuaries. In the conventional system of command and control, the monitoring organization focuses on limiting the concentrations of physicochemical parameters of water, such as dissolved oxygen (DO), biochemical oxidation demand (BOD), chemical oxidation demand (COD), total nitrogen (TN), total phosphorous (TP), total kjeldahl nitrogen (TKN), and etc in the effluents of point-sources. This framework is easy for monitoring and penalizing, particularly for industrial and domestic polluters with continuous annual discharge flow. However, it has several shortcomings. The main weakness is the inflexibility of water quality standards regarding the environmental conditions of rivers, their self purification and vulnerability potential, and the seasonal variations of water quality and quantity of rivers. Besides, the conventional approach neglects controlling the discharges of non-point sources (NPS), including agricultural activities, as they may not be continuous or precise in location for sampling. These faults are introduced as a reason of pollution accumulation and Eutrophication in surface waters. In the second approach termed as controlling ambient discharges, the water quality standards are determined in local scales regarding the environmental potential and conditions of rivers. Here, water quality monitoring is focused on the critical points in the river itself and limiting the pollution loads rather than concentrations in these stations. This approach in monitoring considers other issues like the self-purification potential of river, and the total pollution loads (TPL) discharged by both point and non-point sources upstream. However, there are some challenges that make this framework more complicated. 1) Finding a proper standardization and TPL in a multi-parameter framework, 2) waste load allocation (WLA) and fair sharing of penalties among polluters, and 3) uncertainties regarding the seasonal variations of emissions and the fluctuations in river water quality and quantity. In this research, a methodology is introduced regarding the ambient discharge framework to calculate an optimal multi-parameter WLA among emission sources. This intends to determine an allowable TPL in a river with high seasonal variations and challenges in the aquatic life. For this purpose, we chose Tajan River in northern Iran as the study area. This river has 51 km length with annual average water volume of 15 million m3. It ends to the Caspian Sea where the estuary currently encounters DO deficiency in some seasons and endangers the aquatic life. This may be due to the pollutions discharged from point and non-point sources, including paddy fields, pulp and paper industry and municipal effluents of Sari city with the rural areas upstream. Methodology In order to find a proper WLA and TPL, a simulation is carried out on Tajan River with 18 reaches by Qual2kw software with 100 times iteration for calibration. This simulation includes two steps. In the farming season (FS) of the study area, more than 5 m3/s of water is allocated for paddy fields that reduces one third of river overall flow at headwater. This lessens the remediation potential of river for diluting pollutions discharged particularly the nutrients concentrations exist in the drainage of NPS. Conversely, in non-farming seasons (NFS), DO profile and base-flow of river increases and environmental pollution limits to the point sources. Therefore, simulation is calibrated with respect to the sampling results in the first scenario of FS and later validated by other data in NFS. Regarding the fitness function and auto-calibration based on the genetic algorithm, the simulated model with 100 iterations presented 71% accuracy. For that, the water quality data sampled from three stations between 2014 and 2015 in the upper, middle and lower lands of river are used. Results Figure 1 illustrates DO deficiency of river in two periods. It is obvious that in FS, DO deficiency exceeds 2. 5 mg/L (for a DO saturation of 8. 5 mg/L) that endangers aquatic life in the last 15 km of river to the terminus point but this is rather normal in NFS. Besides, in FS the concentrations of nutrients like TN and TP respectively increases more than 5 and 1. 5 folds in comparison with NFS. It should be noted that about 40% of TN is made of TKN in FS that shows two points. First, chemical fertilizers are the main pollution origins of NPS discharges, and second, it may devour considerable amount of DO in the nitrification process. Therefore, NPS like agricultural activities are introduced as the main reason of seasonal pollutions. In addition, both nutrients and carbonaceous compounds are highlighted as influential parameters on DO reduction. Therefore, DO is assumed as the key factor in multi-parameter WLA and decision-making. Here, it is assumed that 5 mg/L should be met as the minimum limit of DO throughout a year even in the most polluted periods FS, while 6 mg/L must be met annually in average. The sensitivity analysis on the origins of pollutions showed that the self purification potential of river for nutrients reduction will not exceed 10%, but it easily reaches 50% for carbonaceous organic loads. This result adds up the significance of NPS pollution control in decision-making for WLA in river. Therefore, regarding the simulated pollution loads of the terminus point in FS and NFS, the annual TPL in WLA is determined in a way that DO profile responds to the assumed limits. As shown in Table 1, the maximum allowable loads of TN and COD are respectively considered 2500 tons/yr and 4500 tons/yr. TPL for other parameters like TKN, nitrate, and TP are respectively 500, 2000, and 250 tons/yr. By these limits, the local concentrations of pollutants can be set as the standard level for better monitoring. For TN, TP and COD the recommended monitoring concentrations are 5, 0. 5 and 9 mg/L, respectively. By these conditions, it is expected that DO remains on the assumed standard level as shown in Figure 2. Here, WLA is set on 45% removal of pollutions discharged by NPS. This value may reduce 34% of TN, 46% of TP and 14% of COD at the terminus point. Conclusion In this research a method in introduced with respect to the ambient-based framework for water quality monitoring to find TPL and consequently the annual average concentrations of main water quality parameters. In the case of Tajan River, it is realized that the estuary is highly sensitive to the seasonal variations of water quality and quantity. The main source of these variations is marked as the agricultural activities of paddy fields that recommended to be mainly focused for multi-parameter WLA and decision-making. For this purpose, it is also recommended that DO is selected as the key controlling index because it reflects the effects of both carbonaceous and nitrogenous compounds and is crucial for the aquatic life. Finally, with respect to the self purification potential of river, TPL and WLA are determined. This approach can be similarly used in other cases to find local standards for water quality monitoring.